Assessing the effectiveness of psychiatric medication in physicians&#39; practices

ABSTRACT

Disclosed is a method of efficiently assessing the effectiveness of psychiatric medications as administered by physicians and more specifically to a methodology that compares the effectiveness of different medications across groups of patients based on changes in mental health scores using electronic systems. Additionally, the invention delineates when changes in medications are made during the course of medical visits for a single patient.

CROSS REFERENCES TO RELATED APPLICATIONS

U.S. Provisional Application for Patent No. 61/338,290, filed Feb. 17,2010, with title “Assessing the Effectiveness of Psychiatric Medicationin Physicians' Practices” which is hereby incorporated by reference.Applicant claim priority pursuant to 35 U.S.C. Par. 119(e)(i).

BACKGROUND OF THE INVENTION

1. Field of Invention

This invention relates to a method of efficiently assessing theeffectiveness of psychiatric medications as administered by physiciansand more specifically to a methodology that compares the effectivenessof different medications across groups of patients based on changes inmental health scores using electronic systems. Additionally, theinvention delineates when changes in medications are made during thecourse of medical visits for a single patient.

2. Description of Related Art

Regarding evaluating medication effectiveness for an individual patient,there are paper (e.g., Kopta, S. M., & Lowry, J. L., “PsychometricEvaluation of the Behavioral Health Questionnaire-20: A Brief Instrumentfor Assessing Global Mental Health and the Three Phases of PsychotherapyOutcome”, Psychotherapy Research, 12, 413-426, 2002) and electronicquestionnaires (e.g., “Behavioral Health Measure-20 within theCelestHealth System-MH, www.celesthealth.com; “OQ-45”, within the OQAnalyst”, www.oqmeasures.com) that assess mental health. Unfortunately,their use with medication treatments requires significant time andeffort to associate questionnaire scores across medical visits withmedication administration and changes in medication. For example, first,the patient is administered the paper-and-pencil questionnaire or anelectronic questionnaire via computer. Second, the physician or staffmust manually or electronically record the mental health score in theclinical notes or on a spread sheet and then record the type ofmedication administered. To detect changes in mental health over time,the notes must be reviewed date by date or the spread sheet visuallyscanned for changes in mental health scores. These changes would thenhave to be associated with the medication being administered at thetime. This medication would also have to be entered into the notes or onthe spreadsheet for the date of each visit.

There is no method available to systematically compare the effects ofdifferent medications across all patients in the physician's practice.

For years, pharmaceutical companies and universities have been comparingmedication effectiveness using the research method of the randomizedclinical trial (RCT). The typical RCT involves several steps (a)administering a psychiatric drug, a psychotherapy, or placebo pill toresearch subjects who are randomly assigned to two or more treatmentconditions; (b) assessing the subjects' mental health at each medicalvisit using a questionnaire administered by paper or an electronicprocess (e.g., computer); (c) statistically analyzing the subjects'scores on the questionnaire; (d) based on these analyses, displaying thepercent of subjects improved (i.e., percent of patients free ofrecurrence) across weeks for the medications being assessed in the RCT.This type of research provides information on medication effectiveness.

Software products are available that monitor across weeks the mentalhealth of individual subjects who are on a medication as prescribed inRCTs. These systems can also calculate the effectiveness of a singlemedication for groups of subjects. For example, Medical Outcome Systems,Inc. (http://www.medical-outcomes.com/indexSSL.htm) supports researchstudies and clinical trials by, among other things, providing graphsthat display a mean mental health score on the ordinate with week numberon the abscissa for a single subject and for all subjects participatingin the RCT.

To understand the effectiveness of the medications that they administer,physicians rely on academic journals that publish the results of theseRCTs. However, the RCT approach to evaluating medication effectivenesshas been criticized along several lines. First, most RCTs are conductedby the pharmaceutical companies who sell the medications which aretested. These companies are not required to publish findings that areunfavorable to their product; therefore, there are concerns about thevalidity of the effectiveness claims made in many medication studies.Second, since most RCT subjects suffer from only one syndrome such asanxiety or depression, they do not represent the typical patientpopulation seen by physicians in the real world. In contrast, thetypical patient seen at the physician's office suffers from acombination of syndromes such as depression with panic attacks ordepression with an eating disorder. These patients usually require acombination of drugs rather than just one medication. Additionally,practicing physicians do not have the time, staff, or statisticalresources to assess in RCT fashion the effectiveness of medications thatthey actually prescribe to their patients.

SUMMARY OF THE INVENTION

In one aspect, the invention relates to a method of efficientlyassessing the effectiveness of psychiatric medications as administeredby physicians and more specifically to a methodology that compares theeffectiveness of different medications across groups of patients basedon changes in mental health scores. Additionally, the inventiondelineates when changes in medications are made during the course ofmedical visits for a single patient.

The present invention and its advantages over the prior art will becomeapparent upon reading the following detailed description and theappended claims with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The above mentioned and other features of this invention will becomemore apparent and the invention itself will be better understood byreference to the following description of embodiments of the inventiontaken in conjunction with the accompanying drawings, wherein:

FIG. 1 shows a prior art Behavioral Health Measure.

FIGS. 2-8 illustrate a method that compares the effectiveness ofdifferent medications across groups of patients based on changes inmental health scores and delineates when changes in medications are madeduring the course of medical visits for a single patient according to anembodiment of the invention. In the preferred embodiment, data andinformation for individuals is collected and stored an electronic systemsuch as a computer database. The data is appropriately combined tocreate measures to efficiently assess the effectiveness of psychiatricmedications.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The invention will now be described in the following detaileddescription with reference to the drawings, wherein preferredembodiments are described in detail to enable practice of the invention.Although the invention is described with reference to these specificpreferred embodiments, it will be understood that the invention is notlimited to these preferred embodiments. But to the contrary, theinvention includes numerous alternatives, modifications and equivalentsas will become apparent from consideration of the following detaileddescription.

The present system, CelestHealth System-MD (CHS-MD) is a softwareproduct that electronically monitors, across visits, the effectivenessof psychiatric medications for an individual patient and also compares,across weeks, the effectiveness of different medications for allpatients in the physician's practice. Using the Behavioral HealthMeasure-20 (BHM-20; see FIG. 1), the CHS-MD evaluates complete mentalhealth in less than 2 minutes.

Medication Outcome Graph: Monitoring Medication Effectiveness for theIndividual Patient

At each medical visit, the patient electronically completes theBehavioral Health Measure-20 (BHM-20; see FIG. 1) that is appropriatefor the patient's Dependent Variable, as will be discussed, on acomputer through the CHS-MD website. The questionnaire is electronicallyscored using a standardized weighted scoring method known in theindustry, generating a score between 0-4.

As will be described, after the patient completes the questionnaire, thephysician immediately receives electronic output pages that indicate thepatient's current mental health status (based upon the patent'sgenerated score) and the medication that s/he is currently taking. FIG.2 shows the Medication Outcome Graph page (MOG) which depicts the BHM-20mental health score (on the ordinate) across visits (on the abscissa).This information is available for at least 12 psychiatric variables thatcan be chosen from the Dependent Variable options which include:

Global Mental Health Depression Well-Being Eating Disorder SymptomsPanic Disorder Life Functioning Suicidality Alcohol Drug Abuse ViolenceTowards Others Anxiety Bipolar Disorder

This page also provides a list of medications prescribed by thephysician; they are labeled Medication 1, Medication 2, and Medication3. Although three (3) medications are shown for example, additionalmedications and treatments could be tracked as well.

For example, FIG. 2 shows a patient with Dependent Variable optionlisted as “Depression”, and generated score for each of the first sixtreatment visits during which the patient was taking Medication 1:Prozac, and Medication 2: Depakote. When a medication is changed at aspecific visit, the physician selects the “Change Prescribed Medication”and selects charged medication from a list of medications stored thatare used to treat the selected Dependant Variable, for example, Busparfor anxiety, Zoloft for depression, and Lamictal for bipolar disorder.As such, the system allows the physician to monitor the effects ofparticular medications and make changes to one to three medications ormore at a time. Referring to FIG. 3, it is illustrated where at theseventh visit, the physician has changed the medication from Prozac andDepakote to Zoloft and Klonipin. At the same time, the trend linechanges from a first color, blue at visits 1-6 to a second color, yellowat visits 7-9.

In addition to color changes other indicia could be used to note achange such as dashed, dotted or lettered lines for example. The resultis a visual que on the graph that makes changes over time easy to see. Apatient could possibly be seeing more than one doctor; for example, thepatient on FIG. 3 might be seeing a general practitioner, a neurologistand a psychiatrist, and each might be independently changing somemedications. The graph tracking method makes communication of changeseasier to track between medical practitioners in the same system oracross systems. It would not be unusual for a mental health patient withother health issues to be taking 6-10 medications that might havevarious interactions.

The colored data points on the trend line correspond to thestatistically determined four levels of mental health Severe Distress(red), Moderate Distress (orange), Mild Distress (yellow), and NormalRange (green). The number at the data point is the patient's specificscore for that treatment visit (see FIG. 2). And, touching a data pointwith the mouse pointer on the output device shows the medication beingtaken at that visit (see FIG. 3).

Using the MOG, the physician and patient receive immediate effectivenessfeedback by viewing the BHM-20 scores for the prescribed medicationsacross visits. Thus, the present system determines the effectiveness ofthe medications administered to the patient across a plurality ofvisits. The method also immediately notes any change in medication atthe treatment visit in which it occurs.

Medication Effect Graph: Comparing the Effectiveness of DifferentMedications for All Patients

As noted above, through the present system, the patient completes theBHM-20, the responses are electronically scored, and then the MOG iscreated for viewing by the physician and patient. The MOG shows thepatient's course of improvement across visits based on the medicationss/he is taking and any changes made in these medications.

This patient outcome information is then statistically analyzed andcombined with the same information from other patients in accordancewith algorithms to create the Medication Effect Graph page (MEG; seeFIG. 4). Such algorithms will generally include combining the totalnumber of patients that (1) meet the selected criteria i.e., not innormal range at intake, selected medications, and dependent variable and(2) exhibit the requested medication effect, i.e., improved, recovered;divided by the total number of patients that meet the selected criteria.The MEG displays the effectiveness of the selected medications acrossweeks for all patients in the medical practice.

By electronic means, each patient's score on each of the at least 12psychiatric variables is compared to statistically determined cut-offcriteria with the patient classified as improved (I), recovered (R) orunimproved (U).

For each of the physician's patients, the week number (e.g., week #1,week #2, etc.) of treatment is calculated. Next, the number of patientswho improve or recover at each specific treatment week number is dividedby the total number of patients treated at that specific treatment weeknumber; the resulting value is a percentage of patients improved orrecovered for each treatment week number.

The MEG displays this information with percent of patients improved orrecovered shown on the ordinate and week number on the abscissa.

As shown in FIG. 4, with input options for the physician including (a)Dependent Variable; (b) Medication Effect Improved or Recovered; (c)Medication 1; (d) Medication 2; (e) Medication 3; (f) Medication 4; (g)Medication 5; (h) Secondary Medication; and (i) Comparison to NationalNorms, and means for operating on the input responses, the physician canselect up to five medications to compare for one graph. For example, inFIG. 4 the anti-depressant drug medications listed are: (a) Effexor, (b)Lexipro, (c) Paxil, (d) Zoloft, and (e) Prozac. For the dependentvariable of Depression, there is shown five MEG lines in FIG. 4. Eachline has a different color a first color, red; a second color, orange; athird color, yellow; a fourth color, green; and a fifth color, tan.These line colors correspond to the same colored frame around each ofthe five medications displayed. The data points are one color black.When the mouse pointer touches the data point on the output device, thesystem will display the number of patients assessed at that data pointand the specific percentage of patients improved or recovered is shown(see FIG. 4).

Additionally, a combination of two medications can be assessed foreffectiveness. As shown in FIG. 5, three anti-depressant drugs (a)Effexor, (b) Lexipro, (c) Paxil are each combined with the moodstabilizer Depakote in the MEG. The Dependent Variable is againDepression. That is, for these patients, their physicians used one ofthe three anti-depressants with Depakote, as a secondary medication.

The system will also generate results based upon a selected physician'spractice or based upon the results of all physicians entered “Allpractices.” As such, if Physician's Practice is selected, the MEGinformation is based on patients in the specific physician's practice orspecific group practice. If All Practices are selected, then the MEGinformation is processed using all medical practices in the presentsystem.

The MEG information is updated in the system according to a specifictime period, for example, every 24 hours. Thus, the MEG provides medicaleffectiveness information in the form of a living, dynamic process.

Software systems that electronically assess the mental health of anindividual patient are already available as commercial products.Regarding an individual patient, the CelestHealth System-MH assesses theeffectiveness of psychotherapy as a singular, generic treatment processacross sessions and allows selecting the relevant Dependent Variable. Itprovides a psychotherapy outcome graph with date on the abscissa andmental health score on the ordinate (see FIG. 6). Additionally,regarding a group of patients, the system shows psychotherapeuticeffectiveness across sessions for all patients in a privatepsychotherapy practice. It provides a psychotherapy effect graph withsession number on the abscissa and percentage of patients improved orrecovered on the ordinate (see FIG. 7).

The present system is different from the prior art because in additionto monitoring mental health across visits, it monitors mental healthacross visits for combinations of medications as well as changes inmedication for the individual patient which are immediately displayed onthe electronic MOG page. Whereas prior art systems may have thecapability to monitor the effectiveness of combinations of mental healthtreatments for an individual patient across visits, they do notdelineate changes in these treatments during the treatment process.Uniquely, the present system's monitoring method allows the physician toinput medication options that immediately delineate which medicationsare being prescribed for the patient and any changes in thesemedications as they occur during treatment visits. This information isdisplayed in the MOG (see FIGS. 2 and 3).

The present system has the capability to immediately collect medicationeffect data from many physicians' practices and for a specific timeperiod (e.g., every 24 hours) automatically create MEG's based on thenational, normative data base that is characterized by large numbers ofpatients treated in all practices using the system. These graphs can beautomatically updated according to the time period as data from the manypractices continue to be input into the system. This process provides aliving, dynamic display of medication effectiveness findings. There areno medication or psychotherapy evaluation systems that collect andanalyze data in this fashion. The OQ Analyst does offer a graph showingnational norms from different settings such as inpatient mental health,outpatient mental health, and employee assistance; however, the data iscollected only once at a specific point in time and remains static invalue. The data for these norms are not changing due to the repeatedinput of data as is capable with the present system. An example of thebenefits of this kind of system not currently possible would includedetection of counterfeit drugs. On rare occasions drugs have beencounterfeited and these instances can go undetected for years. With thepresent system, if counterfeit Depakote for example, were introduced ina community, all patients taking the drug would likely suddenlyexperience negative effects. With the current invention a graph of allpatients in a practice would have a good chance of showing a counterfeitdrug in just a few weeks. The system can be set to automatically detectpositive or adverse changes of a certain percentage of a doctor'spatients or a percentage change of a doctor's patients versus a largergroup such as a national group.

The present system has at least two unique capabilities. First, itprovides a monitoring system that allows the physician to immediatelydelineate on the MOG electronic page which medications are beingprescribed for the patient and make any changes in these medications asthey occur across treatment visits. These changes are displayed on theMOG. Second, medication effectiveness data from all physicians'practices using the system which create a national, normative data baseare displayed on the MEG and function as a living, dynamic process. Thisinformation is automatically updated according to a specific time periodfor example, every 24 hours as new data is inputted into the system.

The CHS-MD's methodology has the capability to assess the effects ofmedications for other medical specialties. For example, in the area ofpain management, instead of using a mental health questionnaire, a painassessment questionnaire such as, the Dallas Pain Questionnaire (Lawlis,Cuencas, Selby, & McCoy, 1989) would be electronically administered tothe patient at each visit. DPQ pain scores would be electronicallycalculated and presented in the MOG format that includes fourcolor-coded levels of pain severe (red), moderate (orange), mild(yellow), and none (green). The medication drop down menus would listmedications for controlling pain for example, Vicodin, Percocet,OxyContin rather than psychiatric medications. The dependent variabledrop down menu would feature the scales of the DPQ as variables forexample, Daily Activities, Work/Leisure, Anxiety/Depression, and SocialActivities rather than psychiatric variables. The physician wouldmonitor the patient's pain scores instead of mental health scores acrossvisits. He would use the MOG to help in his decision to continue orchange medications and would use the MEG to help in decisions aboutwhich medications are best for which types of pain.

FIG. 8 shows the steps of the method 1500. The drugs and therapy apatient is currently on are electronically recorded 1502. The patientcan then complete an electronic health survey questionnaire 1504. Thequestionnaire is scored electronically 1506 and the results areautomatically and electronically charted. Based on the questionnaireexamination and past data, the physician will make a decision to changemedication 1508. A follow-up appointment will be scheduled 1510 and datawill be charted for the patient 1512. An algorithm will be applied totrack progress of patient across that patient's sessions 1514, againstother patients on the same treatment and having the same doctor 1516,and results are compared against a population such as patients takingthe drug nationally. If the system detects a statistically significantresult 1520 such as one patient doing much worse than others in apopulation then that result will be automatically flagged for the doctorto provide further follow-up.

While this invention has been described in conjunction with the specificembodiments described above, it is evident that many alternatives,combinations, modifications and variations are apparent to those skilledin the art. Accordingly, the preferred embodiments of this invention, asset forth above are intended to be illustrative only, and not in alimiting sense. Various changes can be made without departing from thespirit and scope of this invention.

1. A method of administering effective drug treatment including thesteps of: providing a computer based mental health questionnaire;prescribing a first drug treatment; assigning a dependent variable to apatient; tracking effectiveness of said first drug treatment bycompleting the mental health questionnaire repeatedly; plotting theresults of said tracking over time; providing a visual display of saidresults over time wherein said visual display includes a visual displayof said first drug treatment and wherein said drug treatment can bechanged from a set of choices; prescribing a second drug treatment toreplace said first drug treatment in response to said results.
 2. Themethod as recited in claim 1, wherein when said first drug treatment ischanged to said second drug treatment said visual display will change toshow said second drug treatment.
 3. The method as recited in claim 1,wherein the steps of completing the mental health questionnairerepeatedly includes tracking a data point on said visual display andwherein a statistically significant event automatically detected bycomparing said results with results for other patients is reported, andincluding the step of comparing the results for at least one of adoctor's patients having the first drug treatment and dependent variablewith the trend lines for all patients having the same drug treatment andthe same dependent variable.
 4. A method of administering effective drugtreatment including the steps of: providing a computer basedquestionnaire; generating a score based upon a patient's response tosaid questionnaire; assigning a dependent variable to said patient;prescribing a first drug treatment based upon said dependent variable,wherein said first drug treatment includes at least one medication;tracking effectiveness of said first drug treatment; completing saidquestionnaire repeatedly on a regular basis and generating a new scorebased upon said questionnaire responses; plotting each score generated;means to determine a level of health based upon each of said scores andsaid dependent variable; providing a visual display of said scores,wherein said display includes a trend line of said first drug treatmentand wherein said trend line corresponds to said level of mental health;prescribing a second treatment in response to said trend line.
 5. Themethod as recited in claim 4, wherein when said first drug treatment ischanged to said second drug treatment said visual display will change toshow said first and second drug treatments.
 6. The method as recited inclaim 4, wherein the step of completing the questionnaire repeatedlyincludes tracking a data point on said trend line and comparing thetrend lines for all of a doctor's patients with the same drug treatmentand dependent variable with the trend lines for all patients having thesame drug treatment and dependent variable.
 7. The method as recited inclaim 6, wherein each of said tracking points on said trend linecorrespond to a color, and each color corresponds to a level of healthand wherein a statistically significant result is detected by comparingsaid trend lines for other patients and said statistically significantresult is reported.
 8. The method as recited in claim 7, whereintouching said tracking point with a pointer displays the patient's drugtreatment at the time said tracking point was generated.
 9. A method ofadministering effective drug treatment including the steps of:completing a computer based questionnaire; generating a data point basedupon a patient's response to said questionnaire; assigning a dependentvariable to said patient; prescribing a first drug treatment based uponsaid dependent variable and said data point; tracking effectiveness ofsaid first data treatment by said patient periodically completing saidquestionnaire and generating a new data point base upon saidquestionnaire responses; plotting each data point generated; assigningeach data point to a level of health; providing a visual display of saiddata points over time, said visual display including a visual display ofsaid first drug treatment and further including a trend line thatcorresponds to said level of health, and wherein said drug treatment canbe changed; prescribing a second drug treatment based on said trendline.
 10. The method as recited in claim 9, wherein when said first drugtreatment is changed to said second drug treatment said visual displaywill change to show said first and second drug treatments.
 11. Themethod as recited in claim 9, wherein the step of periodicallycompleting the questionnaire includes tracking said data point on saidtrend line.
 12. The method as recited in claim 11; wherein each of saiddata points on said trend line correspond to a color, and each colorcorresponds to a level of health.
 13. The method of recited in claim 9,wherein touching said data point with a pointer displays the patient'sdrug treatment at the time said data point was generated.
 14. The methodas recited in claim 9, wherein the step of providing a visual display ofsaid data points over time includes combining said data points with datapoints generated from other patients having the same drug treatment. 15.The method as recited in claim 14, including the step of generating apercentage of patients that improved over time with said same drugtreatment.
 16. The method as recited in claim 12, including the step ofcomparing the trend line with trend lines for at least one other patientand detecting significant differences.
 17. The method as recited inclaim 12, including the step of comparing the trend lines for all of adoctor's patients with the same drug treatment and dependent variablewith the trend lines for all patients having the same drug treatment anddependent variable.